{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0                       1  2        3       4       5      6   7  \\\n",
       "0  162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1  162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2  162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3  162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4  162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取文件\n",
    "df = pd.read_csv('./log.txt', sep = '\\t', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2  162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3  162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4  162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置列名\n",
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96922</th>\n",
       "      <td>7210901</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>485.91</td>\n",
       "      <td>112.04</td>\n",
       "      <td>252.71</td>\n",
       "      <td>161.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-24 23:46:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52664</th>\n",
       "      <td>4354348</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>819.15</td>\n",
       "      <td>105.83</td>\n",
       "      <td>179.89</td>\n",
       "      <td>136.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 13:30:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97066</th>\n",
       "      <td>7224822</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>418.74</td>\n",
       "      <td>105.06</td>\n",
       "      <td>177.63</td>\n",
       "      <td>139.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-25 12:51:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100350</th>\n",
       "      <td>7451068</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>1612.52</td>\n",
       "      <td>90.23</td>\n",
       "      <td>237.96</td>\n",
       "      <td>146.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 12:54:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103514</th>\n",
       "      <td>7670033</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1665.21</td>\n",
       "      <td>101.22</td>\n",
       "      <td>361.53</td>\n",
       "      <td>185.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-04 23:30:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                     api  count  res_time_sum  res_time_min  \\\n",
       "96922   7210901  /front-api/bill/create      3        485.91        112.04   \n",
       "52664   4354348  /front-api/bill/create      6        819.15        105.83   \n",
       "97066   7224822  /front-api/bill/create      3        418.74        105.06   \n",
       "100350  7451068  /front-api/bill/create     11       1612.52         90.23   \n",
       "103514  7670033  /front-api/bill/create      9       1665.21        101.22   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "96922         252.71         161.0        60  2019-02-24 23:46:36  \n",
       "52664         179.89         136.0        60  2019-01-01 13:30:57  \n",
       "97066         177.63         139.0        60  2019-02-25 12:51:37  \n",
       "100350        237.96         146.0        60  2019-03-01 12:54:42  \n",
       "103514        361.53         185.0        60  2019-03-04 23:30:46  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) # 随机采样，多次执行结果不一样，看数据大概"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看api列特征\n",
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "2  162742      5        845.84        136.31        225.73         169.0   \n",
       "3  162808      9       1305.52         90.12        196.61         145.0   \n",
       "4  162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  \n",
       "2        60  2018-11-01 00:02:07  \n",
       "3        60  2018-11-01 00:03:07  \n",
       "4        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('api',axis = 1) # 优化内存，删除指定列\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-02-05 23:17:58\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # 当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将索引替换为时间\n",
    "df.index = df['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看interval列值唯一性特征\n",
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id','interval'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist() #用直方图对count初步分析\n",
    "plt.show()\n",
    "# 结论：接口调用情况大部分在10次以内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30) # 增加柱子数量，看得更详细些\n",
    "plt.show()\n",
    "# 结论：大部分在7-8次每分钟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据，绘制一天时段的接口调用情况\n",
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()\n",
    "# 结论：凌晨时间访问量不多，下午2-3点第一个高峰，晚上8-9点第二个高峰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用count重采样，一个小时为单位，会看起来比较平滑\n",
    "df2 = df['2019-5-1']\n",
    "df2 = df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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byoLCDDKTI6c/YNCs3FTmFaRF/drDmgSUCqOaRg/zi9JJig/voinjsXpOLlkp8Tyz80RYrtfrHeCt+jMR2RQ0aO3CQt6oa+PM2T67QwkZTQJKhYnPZ9jZ0G5r0bjRxLtdXLuwkBf2NoWliubOxnZ6vZEzP2A4lWWF+Az8aV/0NglpElAqTOpaz9LZ643YJAD+JqGzfQO8ciD0o4S2HvHXK1oeQfMDzreoJIPizKSoXntYk4BSYWLncpLjdcnsHLJTE3gmDOWlq+ramF+YzpTUhJBfa7L8y04WsulQM+f6onONAU0CSoVJTYOHlAQ3c/LT7A5lRHFuF9cuKuTFfU0hven1eX28eSyy+wMGVZYV0NPv488HwzuHIlw0CSgVJjWN7SwuycRtw3KSE3H94iK6+wZ4OYRr7e467uFc/0DETRIbzsWzsslMjo/aUUKaBJQKgz6vj70n7F1OcryWz8omNy0hpLWEtta2vX2tSBfvdrFmQT4v7jtt++I7oaBJQKkw2H+qg74BX0R3Cg+Kc7t496IiXtzfRHefNyTXqKprY15BGjlpiSE5f7BVlhXSfq6fN+ra7A4l6DQJKBUGNW+Xj46smkEjWVdeRE+/jxf3Bb9JqH/Ax/ajbayYFfn9AYOumJdHUrwrKkcJaRJQKgxqGjzkpCZQkpVsdyjjcvHMbPLSE0PSJLT7eDvdfQOO6BQelJzg5rK5eWzccyrq1hgIKAmIyOdEZI+I7BaRh0XE3gVTlYpQNQ2RsZzkeLldwrrFRbx84DRdvcFtEqqqc05/wFCVZQWcaO9h9/EOu0MJqkknAREpAT4NVBhjFgFu4OZgBaZUtOjq9XK4ucsR/QFDrSsvotfr48Ugz5bdWtvKnPw08tKd0R8w6F0LCnAJUTdKKNDmoDggWUTigBQgPEVHlHKQXY3tGAPlDukPGHTR9CkUZiQFtby0d8DH9qNnHDE09HxTUhNYPis76tYYmHQSMMYcB74P1AMngXZjzMbz9xORu0Rku4hsb26OzskWSo3m7fLRDnsScLmE6xYX8ecDzXT29AflnHtPdtDV62WFg/oDhrqmrJCDTV1RtcZAIM1BU4AbgVlAMZAqIredv58x5l5jTIUxpiIvL2/ykSrlUDUNHqZnp5AdweURRrKuvIi+AV/QCqhtrfXXC1rpwCcBgEvn5ALwulX3KBoE0hz0LqDOGNNsjOkHfg+sCk5YSkWPnY3tjpgkNpxl07IozkzijzXBaRKqqm1jdm4q+RnOHEMyryCN3LQEXj8cvjUXQi2QJFAPrBSRFPEPeVgD7AtOWEpFh9OdPRz3nGNJhK0pPF4ul7CuvIhXDzXTfi6wJqEBn+GNo20RXTp6LCLCJaW5bD7SGjVDRQPpE6gCHgPeAnZZ57o3SHEpFRV2NgxOEnPmkwDAuvJi+gcMLwQ4UWrfyQ46e7yOmh8wnNWlOTR39nKkucvuUIIioNFBxphvGGPmG2MWGWM+bIzpDVZgSkWDmkYPbpewsDjylpMcryVTMynJSubJ6uMBvfsd7A9w0kzh4ay2+gU2H46OfgGdMaxUCNU0tjM3P42UhDi7Q5k0EeFDK6az6VALD2w+OunzVNW1MSMnhcJMZ/YHDJqWncLUKcm8fiQ6+gU0CSgVIsYYaho8LHVwU9Cgu68o5dqFhfz7M3vZOIlx8j6f4Y26NlY6/Clg0OrSXLYcaWXA5/x+AU0CSoXIsdZu2s/1O7o/YJDLJfzog0spL8nkM49Us8sqiDde+0910n6u39GdwkOtmpNDR4+XvSecX0JCk4BSITI4SazcoSODzpec4OYXt1eQnZrAHb/axnHPuXEfW1Vn9Qc4vFN40CWl/u9jcxQ0CWkSUCpEahraSYp3Ma8g3e5QgiY/PYlffvRievoGuPPBbeOeSby1tpVp2cmOqaI6lvz0JOYVpLE5CuYLaBJQKkRqGj0sKs4k3h1df2bzCtL579su5NDpLj7x2x1jrrY12B/g9FFB51tVmsu2o230eZ292lh0/XYqFSH6B3zsPu7cmcJjuWxuHv/+nkW8erCZrz+1Z9Sho4dOd3Gmu9+RReNGs6o0h55+Hzvqz9gdSkA0CSgVAgebOun1+qKmP2A4tyyfzj9cUcpvq+q5b1PdiPu9XS8oSvoDBq2YnYNLnF9HSJOAUiFQY80UjobhoaP54toLuG5xId95bh/P7x6+vlBVXSslWclMy04Jc3ShlZkcz+KSTMfPF9AkoFQI1DR4yEqJZ3qU3fjO53IJP/zAUpZMzeKzv6umusHzjq8bY6iqbYu6pqBBq+bksqPew9kgr74WTpoElAqBmkYP5VOds5xkIJLi3dx3ewW5aYl87FfbaGj7S639w6e7aD3bF3VNQYNWlebg9Rm2HW2zO5RJ0ySgVJB193k52NTJ0ijuDzhfbloiD370Ynq9Pu54cNvbFUe3WusJR8sksfNVzMgmwe1ydL+AJgGlgmz38Q58xtmVQydjTn46/++2i6hrOcsn1r9F/4CPqtpWCjOSorZZLDnBzbLpWY7uF9AkoFSQ1TQMzhSOrSQA/jby77x3Ma8dbuFf/rCbqro2Vs7OjupmsdVzctlzogNPd5/doUyKJgGlgqy60UNJVjJ56Yl2h2KLD1RM45NXzeGRbQ00d/ZGTamIkayek4MxsMWhTUKaBJQKsmipHBqIf7pmHteXFwFwSZQngfKpWaQmuB3bL6BJQKkgaunqpfHMOZZMi51O4eEMVh197jOXMTM31e5wQire7WL5rGzHFpPTJKBUEA32ByydNsXmSOwX73axoMi5K6pNxKrSXGqbz3KqvcfuUCZMk4BSQVTT4MElsKgkNm5+ym/VHH+TlxNHCWkSUCqIdjR4mFeQ7ujlJNXELSjMYEpKvCP7BTQJKBUkg8tJLpse253CscjlEi4pzeH1wy2jVlSNRJoElAqSupazdPR4WRKD8wOUv1/gRHsPR1u7x945gmgSUCpIBpeTXKpPAjFpVakz+wU0CSgVJNX1HlIS3MzNj57lJNX4zcpNpSgzidcPO6tfQJOAUkFS3djO4pJM3K7oLZGgRibi7xfYUtuKz+ecfgFNAkoFQa93gH0nOmJ+pnCsW12aS9vZPvaf6rQ7lHELKAmISJaIPCYi+0Vkn4hcEqzAlHKSfSc76RvwaRKIcU6cLxDok8BPgOeNMfOBJcC+wENSynkGZwrHWvlo9U5FmcnMzk111HyBSScBEckALgfuBzDG9BljPKMfpVR0qm7wkJeeSFFmkt2hKJutmpNDVW0r/QM+u0MZl0CeBGYDzcAvRWSHiNwnIn9VKUpE7hKR7SKyvbm5OYDLKRW5BiuHRnPdfDU+q0pzOds3wM7GdrtDGZdAkkAccCHwP8aYZcBZ4Mvn72SMudcYU2GMqcjLywvgckpFpvbufmpbzmp/gAL8pbNF4PXDzugXCCQJNAKNxpgq6/+P4U8KSsWUtyeJaRJQwJTUBMqKMhzTLzDpJGCMOQU0iMgF1qY1wN6gRKWUg9Q0eBCBxTG0sLwa3arSHN6sP0NP/4DdoYwp0NFBnwLWi8hOYCnwncBDUspZqhs8lOalkZEUb3coKkKsmpNLn9fH9qNn7A5lTAHVuzXGVAMVQYpFKccxxlDT6OGKefl2h6IiyPKZ2cS5hNePtHDp3Fy7wxmVzhhWKgCNZ87R0tXH0hhfTlK9U2piHEunZbHZAf0CmgSUCsBfOoV1OUn1Tqvm5LKr0UNHT7/doYxKk4BSAaiu95AQ5+KCQq0cqt5pVWkOPgNVtW12hzIqTQJKBaCm0cOi4gwS4vRPSb3TsulZJMW72Bzh8wX0N1epSfIO+Nh1vF3rBalhJca5uXhmNlsivF9Ak4BSk3SgqZOefq0cqka2qjSXA02dNHf22h3KiDQJKDVJNQ3+2jCaBNRIVlulpbfURu7TgCYBpSapuuEMU1LimZ6dYncoKkItLM4kIykuousIaRJQapJqGvz9AVo5VI3E7RJWzs5hcwQvMqNJQKlJ6Or1cvB0J0umalOQGt2q0hwa2s7R0NZtdyjD0iSg1CTsamzHGFg6XZOAGt3qObmkJLg50txldyjDCqh2kFKxanCmsD4JqLHMyU+j+uuVETuXRJOAUpNQXe9hRk4K2akJdoeiIpyIkBAXuf1GkZmalIpwNY0efQpQUUGTgFIT1NTRw8n2Hp0foKKCJgGlJqi6weoP0CSgooAmAaUmqKbBQ5xLWFicYXcoSgVMk4BSE1Td4GFBUQZJ8W67Q1EqYJoElJoAn8+ws7GdJbqSmIoSmgSUmoAjzV109Xp1JTEVNRyRBPq8Pg6c6rQ7DKXe7hTWNYVVtHBEEvjy4zu59b6teAd8doeiYlx1g4f0xDhm56bZHYpSQeGIJFC5sJCWrj5ej/AVelT0q2n0UD4tE5crcmeAKjURjkgCV16QR3pSHE9Wn7A7FBXDevoH2H9SK4eq6OKIJJAU7+bdiwrZsOcUPf0DdoejYtSeE+14fUZnCquo4ogkAHDj0hK6er28tP+03aGoGFWty0mqKBRwEhARt4jsEJE/BiOgkaycnUNeeiJPVh8P5WWUGlF1g4fizCTyM5LsDkWpoAnGk8BngH1BOM+o3C7hb8qLeXl/M+3n+kN9OaX+Sk2DR+sFqagTUBIQkanAOuC+4IQzuvcsK6ZvwMfzu0+G43JKva21q5f6tm5tClJRJ9AngR8DXwRGHMAvIneJyHYR2d7c3BzQxRaXZDIrN1VHCamw29no7w/QJwEVbSadBETkeuC0MebN0fYzxtxrjKkwxlTk5eVN9nKD1+SGJcVsqW2lqaMnoHMpNRHVDR5c4n8jolQ0CeRJYDVwg4gcBR4BrhaRh4IS1ShuWFqMMfB0jT4NqPCpbvAwryCd1ERdkVVFl0knAWPMV4wxU40xM4GbgZeMMbcFLbIRlOalsbgkk6c0CagwMcbocpIqajlmnsBQNy4tZmdjO7XNXXaHomLAsdZuPN39LJ2uSUBFn6AkAWPMK8aY64NxrvG4vrwYEfRpQIVFTaO1nKQ+Cago5MgngcLMJFbOyuGp6hMYY+wOR0W56gYPyfFu5hVo5VAVfRyZBMDfJFTbcpbdxzvsDkVFueoGD4tLMolzO/bPRakROfa3+t2Likhwu7SMhAqpPq+PPSc6tD9ARS3HJoHMlHiuvCCPp3eeYMCnTUIqNPaf6qDP69P+ABW1HJsEwF9ZtKmjl6paXWxGhUaNtZykLiyvopWjk8CaBfmkJri1jIQKmR0NHnLTEinJSrY7FKVCwtFJICnezdpFhTy7+yS9Xl1sRgVXT/8Arx5spmLGFER0OUkVnRydBMDfJNTZ4+WVA4EVp1PqfE9WH6elq48PXzLD7lCUChnHJ4HVpTnkpCbwlDYJqSAyxnDfpjrmF6azqjTH7nCUChnHJ4E4t4vry4v4074mOnt0sRkVHH8+2Myh0138/WWztSlIRTXHJwGAG5aW0Ov1sXFPk92hqChx36Y68tMT+ZslxXaHolRIRUUSuHB6FtOyk3lSawmpINh3soPXDrdw+6qZJMRFxZ+IUiOKit9wEeHGJSVsPtxCc2ev3eEoh7tvUx3J8W5uXTHd7lCUCrmoSALgryU04DM8u0vXH1aT19TRw1M1x/lAxVSyUhLsDkepkIuaJDC3IJ0FRRlaS0gF5NdbjuL1GT66epbdoSgVFlGTBMD/NPBWvYf61m67Q1EO1N3n5aGt9VSWFTAzN9XucJQKi6hKAoMjOZ6q0acBNXGPvdlI+7l+/v6y2XaHolTYRFUSKMlKZvnMbP6gi82oCRrwGR54rY6l07K4aMYUu8NRKmyiKgkA3LC0mMOnu9h3stPuUJSD/GlfE0dbu/nYZbN0cpiKKVGXBK5bXEScS3hSm4TUBNy3qZaSrGSuXVhodyhKhVXUJYHs1ASumJfH09Un8OliM2ocqhs8bDt6hjsunaVLSKqYE5W/8TcsLeZEew/bj52xOxTlAPdtqiU9MY4PVEy1OxSlwi4qk8A1ZQUkx7t1zoAaU+OZbp7bfYpbVkwnPSne7nCUCruoTAIpCXFULizgmV0nae3SMhJqZA9uPgrAR1bNtDUOpewSlUkA4LaVM+jq8XLl91/h/tfq6B/w2R2SijAdPf08sq2BdYuMz5NPAAAP4ElEQVSLKNblI1WMitokcPHMbJ7/7OVcOH0K3/rjXq798au8cuC03WGpCPK7Nxro6vXq5DAV0yadBERkmoi8LCL7RGSPiHwmmIEFw5z8NH51x3J++ZGLMQY+8stt3PHgNmqbu+wOTdnMO+Djl5vrWDErm8VTM+0ORynbBPIk4AU+b4xZAKwEPiEiZcEJK7iump/P85+9nK9dt4BtdW2s/fGrfPuZvXToSmQx69ndpzjR3qNPASrmTToJGGNOGmPesj7vBPYBJcEKLNgS4lz8/eWzeekLV/K+C6dy32t1XP39V/jdtnoGdD5BTPGvH1zL7NxUrp6fb3c4StkqKH0CIjITWAZUDfO1u0Rku4hsb25uDsblApKXnsh331fOU5+4lJk5qXzp8V3c+LPX2Ha0ze7QVJhsO3qGnY3t3HHpLFwuLRGhYlvASUBE0oDHgc8aYzrO/7ox5l5jTIUxpiIvLy/QywXN4qmZPPoPl/CTm5fS2tXHTT/fwqcf3sEJzzm7Q1Mh9otNtUxJied9F+rkMKUCSgIiEo8/Aaw3xvw+OCGFj4hw49ISXvz8FXx6zVw27DnF1T94had0reKoVddylj/ta+K2lTNITnDbHY5StgtkdJAA9wP7jDE/DF5I4ZeSEMc/XTOPFz9/BdeUFVJWlG53SCpEHnitjniXiw9fMsPuUJSKCHEBHLsa+DCwS0SqrW1fNcY8G3hY9pg6JYX/umWZ3WGoEDlzto9H32zgPcuKyU9PsjscpSLCpJOAMeY1QHvVlGOsrzpGT7+POy/VYaFKDQrkSUCpiNfR08+WI628dqiFP1Qf5/J5eVxQqM19Sg3SJKCiSv+Aj+oGD5sOtfDaoWZqGtsZ8BlSEtysmJXN19ZF5HxGpWyjSUA5mjGGI81nee1QM68dbmFrbRtdvV5cAounZnH3FaVcOjeXC6dPISEuaktlKTVpmgSU4/R5fWzYc4pXDzaz+XALJ9p7AJiencINS4u5bE4uq0pzyUzR9QGUGosmAeUYAz7Dk9XH+dGfDtLQdo6MpDhWz8nlE1fnctmcPKbnpNgdolKOo0lARTxjDC/sbeL7Gw9wsKmLhcUZ/PIji7h8Xh5uLfugVEA0CaiI9vqRFu7ZcIAd9R5m56by0w8t47pFRVrzR6kg0SSgItLORg/3bDjApkMtFGUm8d33Lub9F00lzq2du0oFkyYBFVEOn+7kBxsP8tzuU0xJieef1y3gtpUzSIrXOj9KhYImARURGs9085M/HeLxtxpJSYjjs++ay52XziI9SUf4KBVKmgSUrVq6evnpS4f5bVU9CNyxehb/eNUcslMT7A5NqZigSUDZ6v7X6vjN1mPcdNFUPr1mLsVZyXaHpFRM0SSgbPXxy2dz00VTmZ2XZncoSsUkTQLKVlkpCWSlaNOPUnbR8XZKKRXDNAkopVQM0ySglFIxTJOAUkrFME0CSikVwzQJKKVUDNMkoJRSMUyMMeG7mEgzcGySh08H6oMYTrBlAu12BzGKSI4vkmMDjS8QkRwbRH58g/e9GcaYvFBcIKxJIBAi0hyqFyEYROReY8xddscxkkiOL5JjA40vEJEcGzgivpDf95zUHOSxO4AxPG13AGOI5PgiOTbQ+AIRybFB5McX8vuek54EthtjKuyOQymlwiUc9z0nPQnca3cASikVZiG/7znmSUAppVTwOelJQCmlVJBpEhiGiFwrIgdE5LCIfNnadr+I1IjIThF5TESGLYAvIl+xjjsgImtHO2eQ4xMR+baIHBSRfSLy6RGOvV1EDlkftw/ZfpGI7LLO+Z8iIkGO72oReUtEdovIr0Rk2DLmoY5PRB4QkdMisnvItntEZL/1s31CRLLG+31Z22eJSJUV8+9EZNK1sUeI719F5LiIVFsf19kR3wixLRWRrVZc20Vk+QjHhuP3bpqIvGz9/u8Rkc9Y22+y/u8TkRHb18Px841IxhhbPoBrgQPAYeDL1rZZQBVwCPgdkDDCsV+xjjsArB3tnJOIyw0cAWYDCUANUAZkDNnnh8Od39qvBki0vpcj1vmGPWeQ4/so8GvAZe2XP8yx2UCt9e8U6/Mp1tfeAC4BBHgOeHeQ42sA5ln7fBO406b4LgcuBHYP2VYJxFmffw/43ni/L+tr/wvcbH3+c+DuAP4uhovvX4EvTOZ1D2Z8I8S2cfBnAVwHvGLHz9U6VxFwofV5OnDQ+t1bAFwAvAJU2Pj6ReQ9z5YnARFxAz8D3o3/h3SLiJTh/wP8kTFmLnAGuHOYY8uAm4GF+F+A/xYR9yjnnKjlwGFjTK0xpg94BLjRGNNhXV+AZGC4zpQbgUeMMb3GmDr8P5jlI51zErGNGB9wN/BNY4wPwBhzephj1wIvGGPajDFngBeAa0WkCH+S22L8v1m/Bt4TxPjeB/QaYw5a+7xgbQt7fMaYV4G287ZtNMZ4rf9uBaaO8/u60fp9uBp4zNrvV5ONbaT4xink8Y0QmwEyrM8zgRPDHBqO3zuMMSeNMW9Zn3cC+4ASY8w+Y8yBMQ4P6esXyfc8u5qDRrqRjefFDvWNtgT/u9ZBjdY2ROSXwClgPvBf1rYbROSbYxw74jmDGF8p8EHrkfw5EZlrxVchIveNI77GEMZXCMQPeRR/PzDNpvjGcgf+d6SISLGIPDtGbDmAZ0gSCVVsn7Saqx4QkSkRFN9ngXtEpAH4Pv53rLb/XEVkJrAM/7vskfYJ5+sXsfc8u5LASC/4sC92mG+0w7VJGgBjzEeBYvzvMD5obXvKGPP1MY4d8ZxBjC8R6DH+McW/AB6w4ttujPmYzfH58L+T+ZGIvAF0Al6b4huRiHzNimu9FdsJY8xg+7udsf0P/iS/FDgJ/CCC4rsb+JwxZhrwOeB+Kzbbfq7i7697HPjs4BP8cML8+kXsPc+uJDDcC+seZtvgzTecN9pGrHeplqkMecQ1xgzgb7sbrjljpGNHPWeQ4mvE/4sP8ARQPsFjpw6zPWjxWY/8lxljlgOv4m8DtSO+YVmdldcDt1pNE+ONrQXIkr90dAc9NmNMkzFmwGrq+wX+d4CREt/twO+tzx+dYGxB/7mKSDz+v4P1xpjfj7X/OGIM1usXsfc8u5LAcC94PeN7sUN9o90GzLVGBCTgfwf7lIjMgbf7BP4G2D/MsU8BN4tIoojMAubi7/ga9pyTiG3E+IA/4H+0BLgCf6fY+TYAlSIyxWpSqAQ2GGNOAp0istL6/v4OeDKY8YlIPoCIJAJfwt/BZkd8f0VErrViusEY0z2R78tKGC/jb+IC/00xaLFZ8RUN+e/fAruH2c2u+E7g/30D/+/fcMk9LD9X6xz3A/uMMT+c4OGhfv0i9543md7kQD+AOPwjBGbxl574hfjfSQzthf/HYY5dyDtH4NTiz6jDnnOS8V2H/yZ6BPga/mS5GdiF/w9wPdZoIeAG/B2yg8d+zTruAENGOpx/zgBfv786F5AFPGPFuAVYYm2vAO4bcuwd+NsUDwMfHbK9wvrejgA/xZpIGMT47sHfjHYA/2M6dsQHPIy/SaUf/x/Rnda1GoBq6+Pn1r7FwLNj/Qzxjyh5wzrPo0BiAK/dcPH9xvq57sSf8IvsiG+E2C4F3sT/91YFXGTj792l+N8J7xzys7wOf+JsBHqBJvwJKKyvHxF8z5v0jSjQj+Fe8JFebGy40eqHfuiHfgTzI1LveVo2QimlYpjOGFZKqRimSUAppWKYJgGllIphYUkCwxVmEpFPWv83IpI7yrGviEi9NfxrcNsfRKQrHLErpdRkjHDfW29t223N/o4f4diw3fdCngRGqW+xGXgX41t43gOsts6Xhb9QlFJKRaRR7nvr8ZedWYy/BtnHRjxJmO574XgSGKkg2w5jzNFxnuMR/JM3AN7LX2YoIiJpIvKi+MsU7xKRG63t3xKrlKz1/2/LCOWVlVIqyEa67z1rLPiHhg5XrHBQWO574UgCwahv8SJwuZVdb8ZftmFQD/C3xpgLgauAHwyZOXg7gIi4rOPWT+o7UEqpiRn1vmc1A30YeH6Uc4Tlvjfswh5BFoz6FgPAa/iLtiUbY44ObSoDviMil+MvVFYCFFj7tIrIMqAA2GGMaZ3Ud6CUUhMz1n3vv4FXjTGbRjlHWO574UgCE6pvISIb8Ac/tAoh+B+NnsC/wMZQtwJ5+Ker94vIUSDJ+tp9wEfwlzJ+YNLfgVJKTcyI9z0R+Qb+e9bHB79o530vHEng7cJMwHH8jycfGmlnY8zaEb60CfgP/PVLhsoETlsvxFXAjCFfewL/Klbxo11TKaWCbNj7noh8DP8iO2uMtQAU2HvfC3kSMMZ4ReST+CsJuoEHjDF7rM6KL+LPVjtF5NnzMuD55zH4F60433rgaRHZjr9g1P4hx/SJyMv4a3YPBO+7UkqpkY1y36vBPyJyi9W083tjzDdHOU/I73tRXTvI6hh5C7jJGDNciVullIoqE73vRe2MYWtM7mHgRU0ASqlYMJn7XlQ/CSillBpd1D4JKKWUGpsmAaWUimGaBJRSKoZpElBKqRimSUDFHBGZKSITnjwoIg+KyPsncdxHRKR4oscpFQ6aBJSjichkJjzOJLwzyD8CaBJQEUmTgIp4IvJ3IrJTRGpE5DfWO/IfWrMivyciqdYCHdtEZMeQsrozRWSTVW73LRFZZZ3yu8BlIlItIp8TEbeI3GMdv1NEPm4dLyLyUxHZKyLPAPljxPl16xy7ReRe6/j3AxXAeut6yaF7pZSaOJ0noCKaiCzEX0d9tTGmRUSygR8Cufjrsw+IyHeAvcaYh6zFN94AluGv2ugzxvSIyFzgYWNMhYhcCXzBGHO9dY27gHxjzL+LSCL+BY9uss5xN3At/uJee4GPGWMeGyHWbGNMm/X5b4D/NcY8LSKvWNfbHoKXSKmAhKOAnFKBuBp4zBjTAmCMabNqrjw6pC5KJXCDiHzB+n8SMB1/1cafishS/GV5541wjUqgfEh7fyYwF7gcf+IYAE6IyEtjxHqViHwRSAGygT3A0xP6bpUKM00CKtIJw68/cfa8fd5njDnwjgNF/hVoApbgb/rsGeUanzLGbDjv+OtGuPZfn0AkCX+N+ApjTIN17aTRj1LKftonoCLdi8AHRCQH/E0uw+yzAfiUtbIS1oIa4H9Hf9Iq2fth/NUcATqB9POOv9ta7QkRmSciqcCrwM1Wn0ER/hWcRjJ4w28RkTRg6Cii86+nVMTQJwEV0azyu98G/iwiA8COYXb7FvBj/CXJBTgKXI//nfnjInIT8DJ/eXrYCXitsr4PAj/BP2LoLev4ZuA9+OuyXw3sAg4Cfx4lTo+I/MLa9yj+evKDHgR+LiLngEuMMecm9CIoFULaMayUUjFMm4OUUiqGaXOQUhMkIk8As87b/KXzO5aVcgJtDlJKqRimzUFKKRXDNAkopVQM0ySglFIxTJOAUkrFsP8PFL6ppAgPXAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 曲线更平滑，可以看到高峰时间，但是看不到具体时间\n",
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用柱状图看具体时间的访问量\n",
    "plt.figure(figsize = (10,3)) # 单位是英寸\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aeCAi/gx4FfjTxuYHgcci4gec+4fgdAtdzAF/GxEnU0rl7v8FUnu8bFGSMuGUiyRlwkCXpEwY6JKUCQNdkjJhoEtSJgx0ScqEgS5JmTDQJSkT/we27EYkkQxuhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析有没有异常时段，访问接口过于频繁\n",
    "df['2019-5-1'][['count']].boxplot(showmeans = True, meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 20:16:13</th>\n",
       "      <td>21</td>\n",
       "      <td>2992.24</td>\n",
       "      <td>86.28</td>\n",
       "      <td>246.71</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2018-11-03 20:16:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:01:13</th>\n",
       "      <td>22</td>\n",
       "      <td>3615.11</td>\n",
       "      <td>108.00</td>\n",
       "      <td>231.49</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2018-11-03 22:01:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:42:13</th>\n",
       "      <td>28</td>\n",
       "      <td>4332.65</td>\n",
       "      <td>76.26</td>\n",
       "      <td>263.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2018-11-03 22:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 15:49:17</th>\n",
       "      <td>24</td>\n",
       "      <td>3723.64</td>\n",
       "      <td>88.97</td>\n",
       "      <td>280.92</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-05 15:49:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 19:33:17</th>\n",
       "      <td>21</td>\n",
       "      <td>2831.71</td>\n",
       "      <td>78.66</td>\n",
       "      <td>170.69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2018-11-05 19:33:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06 20:49:20</th>\n",
       "      <td>21</td>\n",
       "      <td>3414.39</td>\n",
       "      <td>87.02</td>\n",
       "      <td>257.39</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-06 20:49:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 15:56:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3356.42</td>\n",
       "      <td>85.43</td>\n",
       "      <td>252.38</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2018-11-08 15:56:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:50:23</th>\n",
       "      <td>23</td>\n",
       "      <td>3998.72</td>\n",
       "      <td>90.64</td>\n",
       "      <td>398.60</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2018-11-08 20:50:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:51:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3736.10</td>\n",
       "      <td>87.71</td>\n",
       "      <td>327.77</td>\n",
       "      <td>177.0</td>\n",
       "      <td>2018-11-08 20:51:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:59:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3161.50</td>\n",
       "      <td>89.86</td>\n",
       "      <td>423.33</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2018-11-08 20:59:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 20:49:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3962.84</td>\n",
       "      <td>129.44</td>\n",
       "      <td>322.40</td>\n",
       "      <td>188.0</td>\n",
       "      <td>2018-11-09 20:49:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 21:41:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3199.91</td>\n",
       "      <td>75.82</td>\n",
       "      <td>276.96</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-09 21:41:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 22:09:25</th>\n",
       "      <td>22</td>\n",
       "      <td>3582.53</td>\n",
       "      <td>108.02</td>\n",
       "      <td>246.32</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-09 22:09:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 20:07:26</th>\n",
       "      <td>22</td>\n",
       "      <td>3362.64</td>\n",
       "      <td>80.28</td>\n",
       "      <td>225.21</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-10 20:07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:17:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3407.67</td>\n",
       "      <td>100.55</td>\n",
       "      <td>263.82</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-10 21:17:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:48:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3274.11</td>\n",
       "      <td>84.12</td>\n",
       "      <td>354.66</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-10 21:48:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 22:03:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3525.31</td>\n",
       "      <td>119.81</td>\n",
       "      <td>283.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-10 22:03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 17:02:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3123.46</td>\n",
       "      <td>68.51</td>\n",
       "      <td>359.94</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-11 17:02:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:45:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3515.21</td>\n",
       "      <td>85.81</td>\n",
       "      <td>297.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-11 20:45:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:48:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3006.97</td>\n",
       "      <td>83.48</td>\n",
       "      <td>353.50</td>\n",
       "      <td>143.0</td>\n",
       "      <td>2018-11-11 20:48:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 22:17:28</th>\n",
       "      <td>23</td>\n",
       "      <td>3709.56</td>\n",
       "      <td>92.62</td>\n",
       "      <td>314.90</td>\n",
       "      <td>161.0</td>\n",
       "      <td>2018-11-11 22:17:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 16:28:30</th>\n",
       "      <td>22</td>\n",
       "      <td>3328.76</td>\n",
       "      <td>78.25</td>\n",
       "      <td>257.35</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 16:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:01:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3177.52</td>\n",
       "      <td>92.07</td>\n",
       "      <td>226.59</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 21:01:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:06:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3887.31</td>\n",
       "      <td>100.05</td>\n",
       "      <td>292.41</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2018-11-12 21:06:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-13 15:51:32</th>\n",
       "      <td>23</td>\n",
       "      <td>3505.80</td>\n",
       "      <td>78.76</td>\n",
       "      <td>249.86</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-13 15:51:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 16:08:18</th>\n",
       "      <td>27</td>\n",
       "      <td>13177.00</td>\n",
       "      <td>80.89</td>\n",
       "      <td>2768.33</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2019-05-27 16:08:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 18:29:18</th>\n",
       "      <td>23</td>\n",
       "      <td>5264.64</td>\n",
       "      <td>90.01</td>\n",
       "      <td>515.05</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2019-05-27 18:29:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:28:18</th>\n",
       "      <td>21</td>\n",
       "      <td>4612.10</td>\n",
       "      <td>93.98</td>\n",
       "      <td>372.50</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2019-05-27 19:28:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:49:18</th>\n",
       "      <td>28</td>\n",
       "      <td>5647.21</td>\n",
       "      <td>78.28</td>\n",
       "      <td>648.65</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2019-05-27 19:49:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:03:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5146.42</td>\n",
       "      <td>97.18</td>\n",
       "      <td>1250.87</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2019-05-27 20:03:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:05:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5242.64</td>\n",
       "      <td>113.51</td>\n",
       "      <td>507.65</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2019-05-27 20:05:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:13:18</th>\n",
       "      <td>26</td>\n",
       "      <td>4656.33</td>\n",
       "      <td>102.24</td>\n",
       "      <td>300.69</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2019-05-27 21:13:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:16:18</th>\n",
       "      <td>24</td>\n",
       "      <td>5160.23</td>\n",
       "      <td>95.19</td>\n",
       "      <td>538.70</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-27 21:16:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:58:18</th>\n",
       "      <td>25</td>\n",
       "      <td>9587.37</td>\n",
       "      <td>97.71</td>\n",
       "      <td>1304.84</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2019-05-27 21:58:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 22:01:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5813.94</td>\n",
       "      <td>118.05</td>\n",
       "      <td>1130.25</td>\n",
       "      <td>276.0</td>\n",
       "      <td>2019-05-27 22:01:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 16:19:19</th>\n",
       "      <td>24</td>\n",
       "      <td>5168.07</td>\n",
       "      <td>94.52</td>\n",
       "      <td>869.76</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-28 16:19:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:51:19</th>\n",
       "      <td>23</td>\n",
       "      <td>7090.56</td>\n",
       "      <td>89.50</td>\n",
       "      <td>1613.17</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2019-05-28 20:51:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:52:19</th>\n",
       "      <td>23</td>\n",
       "      <td>5801.02</td>\n",
       "      <td>77.39</td>\n",
       "      <td>802.72</td>\n",
       "      <td>252.0</td>\n",
       "      <td>2019-05-28 20:52:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 22:53:19</th>\n",
       "      <td>22</td>\n",
       "      <td>4000.22</td>\n",
       "      <td>83.75</td>\n",
       "      <td>356.17</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2019-05-28 22:53:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 16:02:20</th>\n",
       "      <td>23</td>\n",
       "      <td>10137.39</td>\n",
       "      <td>96.03</td>\n",
       "      <td>1245.05</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2019-05-29 16:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 20:31:20</th>\n",
       "      <td>22</td>\n",
       "      <td>8799.29</td>\n",
       "      <td>105.93</td>\n",
       "      <td>2386.80</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2019-05-29 20:31:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:12:20</th>\n",
       "      <td>21</td>\n",
       "      <td>4702.18</td>\n",
       "      <td>97.59</td>\n",
       "      <td>699.19</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:12:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:34:20</th>\n",
       "      <td>24</td>\n",
       "      <td>5368.32</td>\n",
       "      <td>73.77</td>\n",
       "      <td>742.53</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:34:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 22:46:20</th>\n",
       "      <td>21</td>\n",
       "      <td>6892.93</td>\n",
       "      <td>137.39</td>\n",
       "      <td>1309.64</td>\n",
       "      <td>328.0</td>\n",
       "      <td>2019-05-29 22:46:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 23:02:20</th>\n",
       "      <td>24</td>\n",
       "      <td>6331.52</td>\n",
       "      <td>103.16</td>\n",
       "      <td>1196.49</td>\n",
       "      <td>263.0</td>\n",
       "      <td>2019-05-29 23:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:02:21</th>\n",
       "      <td>24</td>\n",
       "      <td>5038.76</td>\n",
       "      <td>95.34</td>\n",
       "      <td>445.75</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2019-05-30 20:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:16:21</th>\n",
       "      <td>26</td>\n",
       "      <td>6415.77</td>\n",
       "      <td>85.31</td>\n",
       "      <td>860.74</td>\n",
       "      <td>246.0</td>\n",
       "      <td>2019-05-30 20:16:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:17:21</th>\n",
       "      <td>23</td>\n",
       "      <td>4954.28</td>\n",
       "      <td>97.52</td>\n",
       "      <td>427.05</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-30 21:17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:24:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3977.18</td>\n",
       "      <td>93.16</td>\n",
       "      <td>383.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2019-05-30 21:24:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:28:21</th>\n",
       "      <td>25</td>\n",
       "      <td>8782.18</td>\n",
       "      <td>98.49</td>\n",
       "      <td>2549.79</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-05-30 21:28:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "2018-11-03 20:16:13     21       2992.24         86.28        246.71   \n",
       "2018-11-03 22:01:13     22       3615.11        108.00        231.49   \n",
       "2018-11-03 22:42:13     28       4332.65         76.26        263.33   \n",
       "2018-11-05 15:49:17     24       3723.64         88.97        280.92   \n",
       "2018-11-05 19:33:17     21       2831.71         78.66        170.69   \n",
       "2018-11-06 20:49:20     21       3414.39         87.02        257.39   \n",
       "2018-11-08 15:56:23     21       3356.42         85.43        252.38   \n",
       "2018-11-08 20:50:23     23       3998.72         90.64        398.60   \n",
       "2018-11-08 20:51:23     21       3736.10         87.71        327.77   \n",
       "2018-11-08 20:59:23     21       3161.50         89.86        423.33   \n",
       "2018-11-09 20:49:25     21       3962.84        129.44        322.40   \n",
       "2018-11-09 21:41:25     21       3199.91         75.82        276.96   \n",
       "2018-11-09 22:09:25     22       3582.53        108.02        246.32   \n",
       "2018-11-10 20:07:26     22       3362.64         80.28        225.21   \n",
       "2018-11-10 21:17:26     21       3407.67        100.55        263.82   \n",
       "2018-11-10 21:48:26     21       3274.11         84.12        354.66   \n",
       "2018-11-10 22:03:26     21       3525.31        119.81        283.33   \n",
       "2018-11-11 17:02:28     21       3123.46         68.51        359.94   \n",
       "2018-11-11 20:45:28     21       3515.21         85.81        297.33   \n",
       "2018-11-11 20:48:28     21       3006.97         83.48        353.50   \n",
       "2018-11-11 22:17:28     23       3709.56         92.62        314.90   \n",
       "2018-11-12 16:28:30     22       3328.76         78.25        257.35   \n",
       "2018-11-12 21:01:30     21       3177.52         92.07        226.59   \n",
       "2018-11-12 21:06:30     21       3887.31        100.05        292.41   \n",
       "2018-11-13 15:51:32     23       3505.80         78.76        249.86   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-27 16:08:18     27      13177.00         80.89       2768.33   \n",
       "2019-05-27 18:29:18     23       5264.64         90.01        515.05   \n",
       "2019-05-27 19:28:18     21       4612.10         93.98        372.50   \n",
       "2019-05-27 19:49:18     28       5647.21         78.28        648.65   \n",
       "2019-05-27 20:03:18     21       5146.42         97.18       1250.87   \n",
       "2019-05-27 20:05:18     21       5242.64        113.51        507.65   \n",
       "2019-05-27 21:13:18     26       4656.33        102.24        300.69   \n",
       "2019-05-27 21:16:18     24       5160.23         95.19        538.70   \n",
       "2019-05-27 21:58:18     25       9587.37         97.71       1304.84   \n",
       "2019-05-27 22:01:18     21       5813.94        118.05       1130.25   \n",
       "2019-05-28 16:19:19     24       5168.07         94.52        869.76   \n",
       "2019-05-28 20:51:19     23       7090.56         89.50       1613.17   \n",
       "2019-05-28 20:52:19     23       5801.02         77.39        802.72   \n",
       "2019-05-28 22:53:19     22       4000.22         83.75        356.17   \n",
       "2019-05-29 16:02:20     23      10137.39         96.03       1245.05   \n",
       "2019-05-29 20:31:20     22       8799.29        105.93       2386.80   \n",
       "2019-05-29 21:12:20     21       4702.18         97.59        699.19   \n",
       "2019-05-29 21:34:20     24       5368.32         73.77        742.53   \n",
       "2019-05-29 22:46:20     21       6892.93        137.39       1309.64   \n",
       "2019-05-29 23:02:20     24       6331.52        103.16       1196.49   \n",
       "2019-05-30 20:02:21     24       5038.76         95.34        445.75   \n",
       "2019-05-30 20:16:21     26       6415.77         85.31        860.74   \n",
       "2019-05-30 21:17:21     23       4954.28         97.52        427.05   \n",
       "2019-05-30 21:24:21     21       3977.18         93.16        383.06   \n",
       "2019-05-30 21:28:21     25       8782.18         98.49       2549.79   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "2018-11-03 20:16:13         142.0  2018-11-03 20:16:13  \n",
       "2018-11-03 22:01:13         164.0  2018-11-03 22:01:13  \n",
       "2018-11-03 22:42:13         154.0  2018-11-03 22:42:13  \n",
       "2018-11-05 15:49:17         155.0  2018-11-05 15:49:17  \n",
       "2018-11-05 19:33:17         134.0  2018-11-05 19:33:17  \n",
       "2018-11-06 20:49:20         162.0  2018-11-06 20:49:20  \n",
       "2018-11-08 15:56:23         159.0  2018-11-08 15:56:23  \n",
       "2018-11-08 20:50:23         173.0  2018-11-08 20:50:23  \n",
       "2018-11-08 20:51:23         177.0  2018-11-08 20:51:23  \n",
       "2018-11-08 20:59:23         150.0  2018-11-08 20:59:23  \n",
       "2018-11-09 20:49:25         188.0  2018-11-09 20:49:25  \n",
       "2018-11-09 21:41:25         152.0  2018-11-09 21:41:25  \n",
       "2018-11-09 22:09:25         162.0  2018-11-09 22:09:25  \n",
       "2018-11-10 20:07:26         152.0  2018-11-10 20:07:26  \n",
       "2018-11-10 21:17:26         162.0  2018-11-10 21:17:26  \n",
       "2018-11-10 21:48:26         155.0  2018-11-10 21:48:26  \n",
       "2018-11-10 22:03:26         167.0  2018-11-10 22:03:26  \n",
       "2018-11-11 17:02:28         148.0  2018-11-11 17:02:28  \n",
       "2018-11-11 20:45:28         167.0  2018-11-11 20:45:28  \n",
       "2018-11-11 20:48:28         143.0  2018-11-11 20:48:28  \n",
       "2018-11-11 22:17:28         161.0  2018-11-11 22:17:28  \n",
       "2018-11-12 16:28:30         151.0  2018-11-12 16:28:30  \n",
       "2018-11-12 21:01:30         151.0  2018-11-12 21:01:30  \n",
       "2018-11-12 21:06:30         185.0  2018-11-12 21:06:30  \n",
       "2018-11-13 15:51:32         152.0  2018-11-13 15:51:32  \n",
       "...                           ...                  ...  \n",
       "2019-05-27 16:08:18         488.0  2019-05-27 16:08:18  \n",
       "2019-05-27 18:29:18         228.0  2019-05-27 18:29:18  \n",
       "2019-05-27 19:28:18         219.0  2019-05-27 19:28:18  \n",
       "2019-05-27 19:49:18         201.0  2019-05-27 19:49:18  \n",
       "2019-05-27 20:03:18         245.0  2019-05-27 20:03:18  \n",
       "2019-05-27 20:05:18         249.0  2019-05-27 20:05:18  \n",
       "2019-05-27 21:13:18         179.0  2019-05-27 21:13:18  \n",
       "2019-05-27 21:16:18         215.0  2019-05-27 21:16:18  \n",
       "2019-05-27 21:58:18         383.0  2019-05-27 21:58:18  \n",
       "2019-05-27 22:01:18         276.0  2019-05-27 22:01:18  \n",
       "2019-05-28 16:19:19         215.0  2019-05-28 16:19:19  \n",
       "2019-05-28 20:51:19         308.0  2019-05-28 20:51:19  \n",
       "2019-05-28 20:52:19         252.0  2019-05-28 20:52:19  \n",
       "2019-05-28 22:53:19         181.0  2019-05-28 22:53:19  \n",
       "2019-05-29 16:02:20         440.0  2019-05-29 16:02:20  \n",
       "2019-05-29 20:31:20         399.0  2019-05-29 20:31:20  \n",
       "2019-05-29 21:12:20         223.0  2019-05-29 21:12:20  \n",
       "2019-05-29 21:34:20         223.0  2019-05-29 21:34:20  \n",
       "2019-05-29 22:46:20         328.0  2019-05-29 22:46:20  \n",
       "2019-05-29 23:02:20         263.0  2019-05-29 23:02:20  \n",
       "2019-05-30 20:02:21         209.0  2019-05-30 20:02:21  \n",
       "2019-05-30 20:16:21         246.0  2019-05-30 20:16:21  \n",
       "2019-05-30 21:17:21         215.0  2019-05-30 21:17:21  \n",
       "2019-05-30 21:24:21         189.0  2019-05-30 21:24:21  \n",
       "2019-05-30 21:28:21         351.0  2019-05-30 21:28:21  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看超过20次访问的时段是否是在高峰时段\n",
    "df[df['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析某一天的响应时间，平均响应时间\n",
    "df['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析响应时间异常值\n",
    "df['2019-5-1'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-5-1']\n",
    "df2[df2['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfP7555QNZ6IoSuJSb1sOkWr4tUkir+cwZswYJk2aBMD27dvZsGED/fr1o3PnzsyfP5+uXbuybt06BgwYwCuvvMKQIUNITU0F4PLLL6/y91AUJb6ot8YhliTqeg6zZ89mxowZzJs3j7S0NM477zwKCgoAuP766/n000/p0aMHV111FSJCogZtrC/o3VdqE3Ur1SKJtp7D4cOHadq0KWlpaaxdu5b58+cX77v66qv54osv+Oijj7j++usBOPvss/nqq68oKCggJyeHb775pso6FSWWjJm5geXZh2ItI67QlkMtEryeQ2FhIQBPP/00mZmZDBkyhIKCAowxEddzuOuuuxgzZkyVFs8ZOHAga9asoX///gBkZGTw/vvv06pVq5C8gwYN4s0336RPnz50796dfv36Fe9r2rQpvXr1YvXq1ZxxxhkAnH766VxxxRWcdNJJdOjQgb59+9K4ceOoNSpKrBk9fT2jp6/X8N9B6HoOSrXIyckhIyODvLw8zj33XMaOHcupp55abn79H1WfqSt/4973F3NRr9a8fYuu51ATBXpDWhuisus5aMtBqRZ33303q1evpqCggFtvvbVCw6AoSuKgxiFOSNT1HD788MOakqVUErFDKyVoo19JENQ4xAm6noOiKPFEvRutlKh9KA0B/d8oSuIQ0TiIyDgR2SMiK4PSnhSRHSKy1H5dGrRvuIhsFJF1InJxUPogO22jiAwLSu8kIr+IyAYR+UREkqr6ZVJSUti/f78WQnGIMYb9+/eTkpISaymKolSCyriVxgOvAf8tk/6SMaZUQCAR6QX8ETgBaAvMEJFu9u5/ARcB2cBCEZlsjFkNPGef62MReRO4A3ijKl+mXbt2ZGdns3fv3qocrtQyKSkptGvXLtYyFEWpBBGNgzFmjoh0rOT5hgAfG2MKgS0ishE4w9630RizGUBEPgaGiMga4PfAjXae/wBPUkXj4Ha76dSpU1UOVZSEQZf6UeqC6vQ53C8iy223U1M77Vhge1CebDutvPTmwCFjjLdMelhE5G4RWSQii7R1oCiKUntU1Ti8AXQBTgZ2AS/a6eEqNaYK6WExxow1xvQ1xvRt2bJldIoVRVHCoH2U4anSUFZjzO7AZxF5G/ja3swG2gdlbQfstD+HS98HNBERl916CM6vKIqixIgqtRxEpE3Q5lVAYCTTZOCPIpIsIp2ArsACYCHQ1R6ZlITVaT3ZWCZ7FnCNffytwJdV0aQoilIVtOEQnogtBxH5CDgPaCEi2cATwHkicjKWCygLuAfAGLNKRD4FVgNe4D5jjM8+z/3Ad4ATGGeMWWVf4m/AxyLyNPAr8E6NfTtFURSlSlRmtNINYZLLLcCNMf8A/hEmfQowJUz6ZkpGNCmKotQp2nAIT72bIa0oDQct1pTaQ42DoigNGh2tFB41DoqiJDRauNcOahwURWnQqGkJjxoHRVEUJQQ1DoqSYIhodKVgqutVUq9UeNQ4KIqiKCGocVAUpUFjtNchLGocFEVJaLRorx3UOCiK0qDRPofwqHFQlARFCzWlNlHjoChKQqOT4GoHNQ6KoihKCGocFEVp0GjDIzxqHBRFSWi0bK8d1DgoitKg0XkO4VHjoCiKooSgxkFREgyNrFQaja1UO6hxUBRFUUJQ46AoSoNGGw7hUeOgKEpCox3KtYMaB0VRGjQ6wzo8ahwUJUHRIk2pTdQ4KIqS0FR7tFLNyKh3qHFQFEVRQlDjoChKg0a7HMKjxkFRFEUJQY2DoiQYolOkaxZtOYQlonEQkXEiskdEVgalNROR6SKywX5vaqeLiIwRkY0islxETg065lY7/wYRuTUo/TQRWWEfM0ZEH31FUZRYU5mWw3hgUJm0YcBMY0xXYKa9DXAJ0NV+3Q28AZYxAZ4AzgTOAJ4IGBQ7z91Bx5W9lqIoYdDx+RbVH62k9zEcEY2DMWYOcKBM8hDgP/bn/wBXBqX/11jMB5qISBvgYmC6MeaAMeYgMB0YZO9rZIyZZ6wn/b9B51IURVFiRFX7HFobY3YB2O+t7PRjge1B+bLttIrSs8OkK4qiVIrq1vy1ARaemu6QDtdfYKqQHv7kIneLyCIRWbR3794qSlQURVEiUVXjsNt2CWG/77HTs4H2QfnaATsjpLcLkx4WY8xYY0xfY0zfli1bVlG6oihKCdpwCE9VjcNkIDDi6Fbgy6D0W+xRS/2Aw7bb6TtgoIg0tTuiBwLf2fuOikg/e5TSLUHnUhQlDDqerzTqFqodXJEyiMhHwHlACxHJxhp19CzwqYjcAWwDrrWzTwEuBTYCecDtAMaYAyIyElho5xthjAl0cv8Za0RUKvCt/VIURakTdNRXeCIaB2PMDeXsuiBMXgPcV855xgHjwqQvAk6MpENRFEWpO3SGtKIoCU116/3abgiPGgdFUeKOJdsOUuT1x1pGg0aNg6IkKPW1xrtpbw5Xv/4zI79eXSfX0y6H8KhxUJQEo74XZgdziwBYvesIv39hNte++XOF+bVDuXaI2CGtKIoSKzbvy2XzvtxavYbGVgqPthwUJcHQirJSF6hxUBQloam2rVRjGxY1DoqSYGhZptQFahwURYkr1PjFB2ocFEWJSyobQqr6i/0o4VDjoCgJhg7dVOoCNQ6KkgAcyC2i47BvmLryt1hLqTPqygSqrQ2PGgdFSQDW/XYUgHE/bVE3SFn0htQKahwUJQEITNRqCEs51PR3NMbwzJQ1rNxxOPx+tS5hUeOgKAlE8EI/6g6pHPkeH2PnbOaaCGE4ouG7Vb/x5dId5e43xjBr7Z6E7h9S46AoiUBQGZPA5U1ULN56sFL5qlvzr8r9vOe9xTzw8dJy93+8cDu3j1/IZ4uzq6EstqhxUJQEIFB+SYNwLNUssbhnOw7mA7DrcEGdX7umUOOgKAlAoHZruZXqd9Ohpr9doGVRXguhft/NqqPGQVESCNGGQwiR3EINxQ1X06hxUJQEINivHk1hd80bP/O/H/1aC4oSh0i3K5E7jWsTNQ6KkgAUu5Wi9J8v2nqQr5btrAVFiYPfvnlqAqJDjYOiJADFHdLqVgohcsug4ozacAiPrgSnKAlAsOtDy7IoqcEbVuDx4YjCQieyLdeWg6IkEKJNh6jx12DToMdjUxn08pzi7X//uLnGzh1vqHFQlAQguHhTN0hpInUol3iVaubGBa9p/eGCbTVyznhEjYOiJALFHdJRHFKPrEiBx1flYyMaj+rcpvpzi0NQ46AocYTH52fc3C14ff5S6cWB94KsQyR3ic9fvZLrcL6HTXtzqnWOqhDuay3MOlDl8wVuQ2WMwLcrdnE431Pla9Un1DgoShwxbu4WRny9mvfnbw27XygxFJEK/+pWaoe8NpcLXvyhmmepGSoawhtxtFKEHMH7//zBkqjmheQV+difUxj1NRMBNQ6KEkccKbBqrTmF3lLp4Wq9kYxD2ZZFbqGX+z5cwt6joYVZOLL251UqX9wTZTmdfbDy3/u3IwWc9vSMKAUlBtUyDiKSJSIrRGSpiCyy05qJyHQR2WC/N7XTRUTGiMhGEVkuIqcGnedWO/8GEbm1el9JUeofJbGVpPhzxJZDmd2fL8nmm+W7eGXm+lpQWHPU9ICsYrdSOftDDG/iV/prhJpoOZxvjDnZGNPX3h4GzDTGdAVm2tsAlwBd7dfdwBtgGRPgCeBM4AzgiYBBURTFoiQqawm+CE704JbDyh2Hi1sMiRjZtSKDETG2UpSl/dYD9aTFVE1qw600BPiP/fk/wJVB6f81FvOBJiLSBrgYmG6MOWCMOQhMBwbVgi5FqVdE03IY/Opcxny/EQBH4tmGalHSIW19GDd3Cx2HfVPc8Vz2Lvr8hsN52ildXeNggGkislhE7rbTWhtjdgHY763s9GOB7UHHZttp5aWHICJ3i8giEVm0d+/eakpXlPilotpwYJfXF12fQ4BEnEgXreK8Ii9+fyBUd+n78JE9N+G3CtZayPN4y93XUKiucRhgjDkVy2V0n4icW0HecP9fU0F6aKIxY40xfY0xfVu2bBm9WkVJUAIFXDRDWctrWCSgbaiQsm6jAo+PXo9/xzNT1lj77d2B++FyWsWexx4uHG4eRCTD2xColnEwxuy03/cAk7D6DHbb7iLs9z129mygfdDh7YCdFaQrSoOlbAFeUlRJcWHmjehWCr/f6zPVngNR54QxaK7MFSS3+rpU2qKsA/R4bCoAE5dYS3SWNaJup3WywD04GMaFdM7zs7jgxdnVVZ3QhrjKxkFE0kUkM/AZGAisBCYDgRFHtwJf2p8nA7fYo5b6AYdtt9N3wEARaWp3RA+00xRFKUOplkOUo5UCvDd/K/d9sKTCY3/auC9aaTVGZWcsp7b7gKTmc0ulBa/ZbLBaEb8bNbtUHqfd6eL1Wy2H+z8Mfy827c0Nmx4NG/bU/STCmqI6LYfWwFwRWQYsAL4xxkwFngUuEpENwEX2NsAUYDOwEXgb+AuAMeYAMBJYaL9G2GmKotiULTCTWk4l37muwmMqcjtNXfVbhcfe9O9fgq5tnafI62fmmt0RlFafqMN+BGUvW1MPZwTdjoBbyTqwsus8V6W19eXSnew8lB/1cfFAlUN2G2M2AyeFSd8PXBAm3QD3lXOuccC4qmpRlPqP3edgbyW3mE0Os4F7yj2ipjxHxliF7qjv1vL2j1v45O5+nNm5ec2cPNz1wqRVdvhtcGe7MTBz7Z6QPC7brRRtv8IHv4SftR6JIwUe2pJapWNjic6QVpQEIFDQR+PDrmoIhyJv6bhOgRbIln3W+P/ajj0UruFQ2e/trETGsh3SlaWyM8vLEs36D/GEGgdFSQACBbQglfbJR8q3YMsB1uw6wrCJy5kctJTo2DmbSuX757dr7fNZJ4y2sPvi1x18v7by7qhojVpw7uA5HOUZsSS75RCtcajquhCJOq9EV4JTlAQg2EXkN5Ur1CIVZte9Na/488cLt3PFSW2BUB/8O3O38NjgXsXnc0RZpXzwk6UAZD17WeUOiFAGH87zsHlf+I7eyszhcNlfINJor2A++GUr/5q1KXJG4NWZG1i45WBUmuIRNQ6KEoeULdeD5zn4TOXcOtH2OYyeto6MFBe5haETwDw+f/H5Cj1+HvtiJQ8P6k56kovJy3Yw5KRjcdRQFTmS7FveXcCy7YfI7Bm6b/7m/RHP7wpqOSyqZCjwRyetrHC/MabYCLw4vXTsqsq4uuIRNQ6KkgD4g4yDn9IthwO5RThFaJzmLpUe7aifQHiNgb1ah+z7btVvxRo+XLCNHzfsIy3JSetGKYz4ejUFHj83nHFcVNcrj3Cyg9OWbT9U7r61vx2NeH633eewcU8OD3xstWrSj/8nRfvOx3OoX9hjmqS5OVRBSA2/AWdi2oBy0T4HRakFCjw+/v7Fiip33patbNpD8hEEvylZFW3D7qOcOnI6J42YxkO2+yZATS4El+xyFn/+cYM1B8JvDPvstQzCrWlQk6zedQS/31R7dbuRX69mxmqr/yO4g9nhPkxKmy9C8o/6bi2b9+aQmVJxPfrTRds5/R8zQjrzwbpPh/M8zFoXOnIqntGWg6LUAh8v2Mb787eR5HTy+OW9qn2+4P4Dn7/EOAz9bHnx50m/7uCl608Oe0w0hOtwdjrCj/MP5K1JQxSuQ3rk16sZ+fVqeh/buPzjKiHinblbij+XFOTlH/evWZv4ZGE2aUnOcvMADP98BQDTV4d2vP/lgyXFLZrFf7+Q5hnJEXXGA9pyUJRaIFCOVrWALkvgNN+s2MWOQyUzd8vOki7w+IoLvdnrqhacMpyLPL/IH/a7BPLWZDSO4MukHvcWKce+V7y9Ysfh0PxULpxIWQp9kY0DUNw6qgxNyrj2oLSrqzBMyyJeUeOgKAlAcMH85pwNxZ/LFog9HpvKwJespT2fmLyqStcK13LI9/jCGoBAJ+xLM9Zz/guzAWsVu+rEbjrqOUxmz2G4MpfhSt+Cu1HF3yNwa6IdmlqZlkOAAo8vYh6IPIt65prddBz2DdsTYM2IBmkc/mf8Qt4rZ43esny5dAdTV+6qZUVKfaMmKtLdHv2Wf3yzGihTM5eSgsrnDy0Qs/bn8e8fN1f9wuFaDh5fWLdNcNYt+3Ip8Pg48YnvGPn16oiX+desjcXrKnxsSJTjAAAgAElEQVS6cHvx+XfnWyG1k5r9XCm5Zz37PR2HfUOvx7/DkbqVzJ7DEHfpUUjO9PWIs3SspGIXkET+b+UVVc44RGoZBGI/zY1h7KrK0uCMg89v+H7tHh77ouKhaQEe+Hgp975fcZAyRSmPspXwMTM38POmyhUMRT4/b/9o+chLu3RKCqDyXClPf7MmKp3BhBt0U1BUXsuh9Hahx9I2/uesiLXtUd9ZsaGGTVzOXycu5+dN1jDU6hhWd5OFALjSguYkiIe048aR2r68CD2Rr+gNY4TDUeitnBEJ9FHEMw3OOFT2n6cotcHo6eu58e1fIuZ7Y3bpCVfBtXaRkoJqcxSRQ8V5FEdS5BEzy7ND/fr5Hh+Ltx4slSYioTGPgjYvfeVHNu8tmaxWnttnjz1qqLh2HrV1CGe1Qo2pI7m87x75gsHuImfGasQVeo8A7v/w1wrPsyzo3u6I84B8Dc44/DmoFTAtQmRKRakq1R1ymVvGjVHarRRYpCa6gfXpxz9HepfREfNtC+MPzy0KnRg3ds7mkNAQwd97875cfv/iD8XbXR/9lt1HQiOgBlo/2w7k8eK0dVWICVVNJ55EbhV4ioP0+Ulr/1/SOrxVvWsCA579nrkb4te91OCMww/rS0ZwfPCL5dv0+vw8OmlF+E4iRwE4ancMt6JEwm8MjqTdWLXgQGEWnXEQR5kCXgpBKjcP460fwvdhLC0zIS3SiKFwLrVAv8nIr1fz6vcb2XMk2t9bBOMQsU8hCuPiKLJO6Q7fcoiWm9+J3IqMFQ3OOAQTmJwyZ8NePvhlG0MnLAvJk9n9STK6PRUDdUoiU5PxdLbsy2XEdz+Q3uUlklpOC6rpVu8amT2eIL3LqGqdo2xI7EjDPkd+HdoXUjZ0ds2vUhepZVD564nDbvn4k4KOrd7wVJ/fUODxkRMmbEksadCT4IyBk0ZMK9kusz+wSIdUotmpKAE6DvumSscZYxg7J7SGfv4Ls3GmWTVVZ+o2vDl2UKEo3UrhcLiPlL9TihBnAcbbqNLnu+aNeRXuP5BbFJJWti9i/M9ZpHWkuJYeETElP97iexK+jybs4eH2O+z+AH/pdRjEaaVnJqWS695PyjFf4spYz9E1z5Y9g63BEKkO3uWRKcWfKx2csA5o8C2HYMr+1M569vu6E6PUSw7mFrF4a/nB3XIKvSzeeoCjBR4278vF4/PhavQrIbXRYteIA6miWyla0jqMJaPrMwARw0cEqEzt15jSYTDKc0U5Uyo7hLy6bqPQ/ZndnyKze6jHICPTainl+A6QcfwoXBnrQ/IEcDf7kcyej4Cz+suNxoIGZxxOat+k+HO4GZ97jhZwMEztRlGqwhdLd/KHN+aFFIgB7vzPQv7wxjx6PzkNpwjupvNIPfYT3E0WlclpH2skolupeXpSmNToXTXO1MB6zD6OFnitcziqP8Km0/Ap7A/6jUW7IluA5CJD06OB2nmAMPckUp9DJeY5BDDNP69UPmfaZlJaWy0ChytyMMAAHYd9w1dBa2vEkgZnHIJHV7jKBKb37ljCXx5/mlNGTq9jVUp958ulO5kXJpz0oqyS4aEenx+xC5KyE7ZKdUIHjEM5bqVrTmtXatuZtpHMnsMjahT3fpxpG0PSU9tZ4SvcTRaS2f2pSg2HjURwqOyqDun853gfb73mI5zhS2kziaQWM+ytmutzqOzgFGd6UIvCRFfMvjJzA+t+O8pd/11EkdfPe/O3MjsGQfsanHEIjkXTr3OzUvue/Ow1Rn47sXhbXBX4YxUlDOUNTXzwk6Wl5jcczveEjI6zZteW4yoqrt0KgcJOnIU40zbiSP6NtA6vW6OPAGeZ8aWhrZDwZBw/irQO/w5Jd2VaK8E5M6z38ucLlCPddRhxll6cZ19OdK1zR8q2kLR2AVtbTs3f3dTu/4jYZ1hyvCNlW6VHcFX2nCZK47BxTw5/nbic6at3s3LnYR77YiW3vbuwBjRFR4PrkPYZgyMlG4yDrP2la1iuMs+QKyNyCABFCebDBZULy3LpKz+y41B+qYK89FoEZQu84h5XkprPLk5NbjUV40vDmbYNZ1oWvtzupY46u/cBFh8qbYQyew5jxa0r6P2fYZXSWl0yuv4ToFSnbX4lw1EEEIcHZ2oWxpeKv6jsehPR9Sk4knZz5smr2b5xEDtzt2L8JcVgeqfXw57Bmb4Wd+OKJ7iV0htssKJwWwUorGQsp9qk4RkHP6R3eg2AzxaHG2FgkRdm0k+AZdsPkVfko3+X5jWuT0lsIkVZcDVeiL+gLTsOHQuUHrY5dMKvZPacFfa4wIia0A5QofzC0ccy7/O4MiJoylyBv7BVUIohtAVT3u/BT3LryXgOnYG/sG3FFwqi4lhFYW6icZDW8U2AMCODIvU5lO6jSW33HiuP7iMjqTPprd/AX9iiXCXiOoLxNiLtuPEV6A1oCL52yXcQ/FH3+AQqCsHdVINf/ZFjm6Ty1p/6Rnm2qtHg3EpndmoWNt3VeHGp7QJP6Qd06IRlbNln+YGH/Osnbnh7fu0IVBKaUrN7HaGTKlPbTiS986thj3WUGZ3jTN8QFECu9PPYZadBTPmdsdf3bV8qQF+IzqBSJ7XdB6R3eSnoNN7QfoUww0rbNU1FXEdIajaf9M5j7FFWlWN/bgW++zA1bVNBUZXU/EecaaHrO4szH3fTuWR0ebFUekqSdS7jsH7PjuTyZykHRmtFprzRZZT6P6S2G29FnC1T3gTjTNuEJFmTddf+VuLaXrnjCN+tCl0vorZocMbhzxe0KrWd2uEN0jqPJrXthFLpZVsOny3O5vwXZvPn98v/pypKoMx1pmaR2X0EroxIYbP9IIGCN8j3nbSftOPeIb2zXWgHFTCddxn++R8fV/9kSHY7i49LajYXgIEnNmGrdyo4yvede/wV+NXFgzOt9HyLpmVaH1nPXsZtZ3Us5c9PPfYT21/vA/wkt/oad5MFYS+x81AgjIYfR0o2yW0+Awn85kruw59m+vjD3NIFr7OMuze5xSzSOrwNQIuMkpFaIn5Sjvk65NgiCe0zGTLPT8ffqjH5rowhPrdbkFdBrO8IJf03weWNVQEo+Y5pHd4uNmjh1q6O1iVXVRqccbhz2v8EbflxpW3FGaaD7eznZoXU5AC+XRk+HtPqnUfK/aftyynk00Xbq6RXSSwMgCMPZ5oVTdWZXnHo7OTWX5HZ43HAX2r0kbuJVQkRu4AXKamsNM61CrHu2QYJyuPKWE/rRsl8tf1tVhW+h7uCPrNcT/lj7x3uQzgz1pVKm3jfabSwVzBzN1nAcwues+INlSkUM3s8Rupx7+BM20JS87mktCk99FOcubgaL2bGGqsGnNRiBumdXiOpyaKSET7ix+U1ND9suHyB4fof/aW+f1r7/5Y+Z5BrLrA+dDjEUVTqWC92H48x3DTbz/Pvhv/9ujIjR1BNbjmN5GNKvmuH5iWT51LaTCC902tk9izdx+NI/g1xHSHj+OfJ7PkI6ceX7+YO5sulOyqVr7o0uD6HrCNZxZ/Lb9r5AQdJTSuOe+L3GxwO4WiBh0vH/MigE47hzT+dVirPTf+ez08brWEV/Ts3p32ztGqoV+Idt6uIzO4jireTmv2Eu+nP5Kx9hlKzdl2HEdcR3IFnrIIRMuI6DEFxkTz2rzbJa/C4tuIM/hVnLOHbLd8C4EzLKvec535ybrn7Utu/i8Ndemx+vjefk9s35odsywi9v2Y9HTMWkdQsdNlOV/pmXOUYxZS2H+PK2EBOXgcyjn+h1D6H6yiOJvPxe5oybIKfPlkl96tJ888ob3yT0w9e2yaUHalVEYWNJwHgjlARv/38VD7eUHGepOZWq63wt6tJ6/gan6zLLt7nTAnvCkrv/HKpbYf7UNh8ZbHmnNQ+Da7l0CipJBRAeYV/+vHPkdzKao6+/pqXUf/24kzNCsl31J4Nmm+PLJhaJsrrwdyiYsMA4SNbKnXLvl1b+OW79yJnrAJvz9lMlvkEgNQCw0OTfDTKNdbIFUd+qVp2epdRpHf6V3FHszg83Lznv7TfG+raSD/+OSxXjYXT/pgUxp7kNX6Pox6rYA+0PqIlYBgu/NXP3z+yLpbnyWPV/tIujqyctRErUGUJDA8PCQKINTchpc0XtCrKLmUYAHrsCZ0jEsAZ5HXaw+yo9ED4+xjMxxtCh/eWR2bPYUGTB2sGd5NfrFaHPVQ5+qi1VaPBGYfMpEycPkNqgSn1T8zMC4rX7jpk1QSMocVR6LAXa6REwDcsheDI59znZ1krWeV5cDVejLiOsP1AHi9NX8/UlbtYudOO3CheHEm7OZKvxiFW5Oce4eOhl7Nh8KWkP/gMC6d/FPmYIl9I1NFA2tWPjmbh0qXF6UcLPPxjyho2H7A6Ei9caui/1nDf11bJldT0F8R1hJtm+Th5kz+0cHQUccOs/bz4bx9nrfbbnc0WIn5auhbz6T+9nLnWT5J9aFIUj1N6vqFxTsWFittjSv0O7p5aUnvP9+azL7964aUzew7DnWTNd3A3sYxKy0OGm7/3lfq+bVKmhT0+mKSWU4s/FxuHCmZvH3PA4PZY10guMvTaWmJRwrUchn3q468TQne4vIb2ew0phbVTQGf2HIbDbw02SGr5LeAnpY3VwklpMwlx5hQvqFTbNDi3Uus8N8PG+mh9CK4bbn39FocNr79e8iA4/NB2v6HX9pIHID3f4Gn7CYW/XUmzFh8yaP0mvswcDo6mfL9hC6ltJ+AraMvmfRfy6ow1+MXF3wb1ACy/clLTX3hxZjvev/1C3E4HO7aspsUxHUlOVTdTVTHG4PEZvH4/Hp+h0OslNzeHgsO7yTu6F8/h/RTlHGTP8h9p/u1CTjoA29oImXmGzf95idMvuqHcc/v9hktfmc3JWeNp/fS/aNO0Ea99v4EXpq0H4+fbL99m+09vM/W17+nYIp07xlsTzcR2DwUK7lM2Gwas8nMo/VtWdZnKkAmGIfMN1w236mVNjxp6bDf0uuww2H2UD37pJ9njYGMbOJgBOWlCt4OWn/mCpYY5J1quk2T7Gs2PGK762c/4Cx14XaXdKqmFhgI3vPuy9Xx/co6Dhd2Eba2E26f52NdY+OpMS8uwCX56bzXFv4sAT3zgZUqnb0guMvxuhWFhNyGtEHa0sK518WI/i7oK+xuFd+k0PWrwOuFomuC3RwglNbMmqP3fZB/dd8CPJ8DW1tB+jwk7GjW1CC7/xc/eRjC/p4PkFrOL9wWMQ2b3p3B7DWevsu7RMQctjT22G0a872NeD+Glq5zcO8XPgDWG/3en8OK/fXxyTkkd+fJf/Hx1hnDqptKFv9NnyMyHsa9a9/FIKjTKh4lnCV6ncDgdGuXB5wMcdN9ucPkNZ6029NxueOlKJz4n5CXDtT/6cRj4uaewp7Fw1hrDtlawpItgbJfYx8/5+LWz8M/rf8CduQqH3+AXcDdeirvxUkZ//yi3n92JjOTaLb6luouS1BQiMgh4BXAC/zbGVNg707dvX7NoUeVmfgYwXi9rT+xdvL35GGvEWacyLsF1x0L3MH0+EwYIrQ/BgNUGp33b1rSDp8+7j3uXvM7yTkI3fy8u+nYle5rAR7c/T7efRrAvPY817YVjd7fjtIIiul76R5r/7SV2tYC9g84EVzIkJYMrGeNOAXcKJKVBUiomKQ1JToOkTExyOs7kFMSVhNOZjMvhwilOnOLC6XDidDislbnsH5cQWK0rcI9BkOKlHcX+UzZNREot/+j1GTw+f3Eh7A0qkL0+Px6/9e71GYp8Xgq9RRT6CijwFVHoLcDjycdXkIMpyIXCHJyFR3AVHcVVmIurKBeXJx+XpwCXpwiXpxCXtwiX14fL68Hl9eH2+nEVvwxur8HlBbfX4PaC22sVxkme0ImMAfY0hf2Xn8WQv77FV1edQrvtXnIe/Qst2nflmI49aNqiHVPeHE7hrOn0G/sD7y3Yyd6vR3LXzF/ZdJwDuXoI92X1BwztizYzdoq12MslVz6LVccygI/+5jUe/zK8W+GNSx38eYol8O2LHdz1nR+fgNPAYzc7Gfl+aE11V1M4lA5+B5ywDY6mWP3WjexK8u4m0Npu3CzpLMw6SWh1CI7dbzhhmyneV5ZXrnDwwGRLy4SzhUa5cPGv1kP95/ucXPSrn6t/Likb/vdeJ7dP83Pq5pK0lcdZD8mJ20qXISs7CNtbwCWLDT/1FAassfbf9LATDPx+mWFVB+HeKT66BYURCtyTH3sJ56wuv1x65QoHLY7ATbNL/tk/nCi8/3sHb77qK/5thmNBN+GM9VaGSf2Fq+bVbPn34lUO/t+k6Gv2O5vCgUxh/EUOXnjHeg6uG+akxRF4/XUfexpDZj68fpmD5Z2EX+5aUeWw8CKy2BgTcbJEXBgHEXEC64GLgGxgIXCDMabc4RZVMQ74/azqfSKOKgb6iif8YnXCeZ1BLwf47M8+B/icYn8WfPa71ykl7yJWHocDr0PwORx4nE5724HH4cQnDpJ9XpJ8XpK9XpL8PtxeH0lev1U4+wxur+XqcHsNSZ6SgjrJLrTLK7Aj4XGCx23wusDjErwu8LnA5xLr5XZgXA58bifG7cS4XZgkN8adBMnJSHIKkpKKq1FzLrhzBOmZVufplDcfodPLk0KuFXAvHE6DKX2P48Tt2fTeUiJ+US8XGYe99AhTcdjQxirAw1UqFKWm6bhiCanu1MgZw1BZ4xAvbqUzgI3GmM0AIvIxMASo8fgV3W5NZc+8PRzKTsVx1Fnl8xw4xkez3yo+/nA67O7Xhja795G50kPLP5/Hjt7nsWbCW7h3H6D1TXciGExhHqYoH1NUgL+oEH9RAXgK8RcVYbxF4CnCeDzg9WI8XvB6wesDnw+8PsTnA68ffH7E58fhMzh9BrFfDo9BCg0OH9bLH/Ruf3b6weWLXBPxuKzC2ucEn9vgc4LfDcblwKQKNBJwOSlKcuJNclKQ5MaRlIQjJQlncgqulBTcqem4UtNISs/EnZ5JUnoTkjOakJTRHHejZjgyGiMZTZGUDHClgqNmu8YuvfcZpiWncnTbBrxHDmJyc5C8fJIP5NJ9o5fGeXDDnG0UJBkKMgwpOdb1+64u38nfdVfJiJk6oYmXY7od5bcFTUN2uTO8eHKsn7Yr1Yc3335O3X4atS3gyNY0XKk+mnXPoeCgmyNby3dtprUsJG9vMqVmADsNVOJZqRHEkN3HwzHbHHiKnKTmWtc1yX6cGNwpfjx5Tvye8m9+o+PyOZqdQmrLIvL3uTE+B5nt8sEBR7dZBWzbfgfZOb8p4vJjvA57jYjS37HtWQdIbebB4TIczkrFGGHvspIBLiktCinYZw33dST5SWlTSO4RF45kQ6rLR+6BJAr8Dlp3ysV/xEnOjtKFe0rzIryAyXHiK6y4bPFlrYKutTtTOl5aDtcAg4wxd9rbfwLONMbcXybf3cDdAMcdd9xpW7dWLo5NKXL2wvb5kLsP0ltCwSE4nA3Nj4ful2DEyU9fv0vPTu1plp6OuFxQeBTTtBMm5yA7aETbNHA2aQepTfBtW8GmlT+zdtkvXP63f7N51XzWzvmSy+57ruSaufug4DA071Lle1Sj+LzgLQBfEXgLwVcI3kKMpwCK8jEFuZiCPExhPqaoEEnLtArs9EZIcga406xXUhq4UqAGVz2LBwq3rmDflmW06XA8jkYtyVuzmJ9/nU9B7kFSneBu0w2PuEhPTqFZ2/bs2b2Lsy+5EeMp5OiMf5HR7QxoeTyOVt3JP7iLrFXzydmzhST89OzVl8UzJ3BG/3NwNmphPRuuFOtZTM6EHYvB78W4MxEHsGsZpLfEJGeyc/tmPHmH6Zh0FHPyzYi/CLN5DuQfRTKbw5Gd0Pk863yePNixCFr2tNyWOxZD5jHQuJ0V48OTC1lzocNZ1nGpzaBld3C4YPdK2LfBOq5lDzA+SMqAohzLWGf9CG1PgSbHwYEtgAF3KjRqZ2nftRLvvoO4e50Bfp+V1rQT7N+EFBy0rtGqJxg/FOVhcveTu3Il6ccfh6Q2wjiTkeR0637kHQC/13rveTnk7oX8g9axxg9FubD1Z8heiOl7B+LNh7Tm0Kit9T2cSdC0o/WPdaXCziWwexX0GAxpTa20A5ut30BRnnXOlEbW9TDWfqfbOt+RndZ9bXKcdQ/zD0BKY/B5rM8tulG4dSfuvFU4upwFmW1g/VQ4ugvanwnuNEybk5EtP1jXSM6EjGPwr50OyY1xNGtj/S7zD0KLbrB+Kn6fCzlxMGbjHKR1T/AXQPszkJbdqvx8J5pb6Vrg4jLG4QxjzP+Wd0yV3EqKoigNnMoah3gZypoNtA/abgfEx4oXiqIoDZB4MQ4Lga4i0klEkoA/ApNjrElRFKXBEhcd0sYYr4jcD3yHNZR1nDEmUsQyRVEUpZaIC+MAYIyZAkyJtQ5FURQlftxKiqIoShyhxkFRFEUJQY2DoiiKEkJczHOoCiKyF6jCLDgAWgDVCzFZO6iu6FBd0aG6oqO+6upgjGkZKVPCGofqICKLKjMJpK5RXdGhuqJDdUVHQ9elbiVFURQlBDUOiqIoSggN1TiMjbWAclBd0aG6okN1RUeD1tUg+xwURVGUimmoLQdFURSlAtQ4KKWQqq49qChKvaJeGgcRyYy1hnCISONYawiHiHQVkQEAJo78jPp/jA4R6SwicbKiVGlEpI2IuGOtI1GIh2e/XhkHEUkXkdeAiSJyo4h0irUmABHJEJHRwAQRuUtEusdaE4CIJInI68DXQFsRSY61Jii+Xy8B74nIVSLSMcaSgFL/x89F5EEROTnWmqCUri+wJkjFDfZvcjRWxOV/ichgOz1mLVT7fr0uIhfHSkN5xFMZVq+MAzACaAQ8DZwCPBtbOSAivwNmAD7gH8DZwIUxFVXCRUArY0x3Y8wEY0xhrAWJiBN41d4cBRwPPBM7RRa2IZgGFAFPYBXCf4mpKEBEegAzgY7GmD7GmF9irakMzwMZwAXAMuAaiF0LVUTaAm8D1wE3ikjEmcJ1TNyUYQlvHETEZb9nAJnAP40xc7AKYoeI/D1GugJN6GzgHmPMw8aYH4BCICsWmmxdwWHaWwLz7fSBInK+iLS3t+v02QjS1QbobYx5yBjzEzAVOFlEyl0yto44DLxhjBlmjJkL/Aj47JpeLPtp8oEFwIcAItJXRE6MtVtCRBwi0gTLiL5kjNkLNAXmi0h6IE8d6nHaHwuAV7BWnmwKDAraFxPitQxLWOMgIj1E5F1ghIh0MMbkYP2zbwQwxhwCngOuEZFjYqDrKVvXJmPMMhFpLiLvAxcDfxKRoSLSLAa6RohIBzu5LdBaRG7DehCvAL4VkfbGGH9dFHph/o/ZwJ6gH0QK1kqBt4hIq9rWE6Srq4j8NbBtjNkCTArKkgd0M8bk1mUtOIyurViG6hIRWQK8BAwH/luXz31ZbcYYv/0bzAeGisgvwG1YteGf6uoZszW9BTwqIp2NMQeAJcaYfOA/wPVAhwpPUnva4rIMC5CQxkFEmgPvAiuxVo57RkQuxPpRXBfUVFwOzAYui5GuESJynb37IDDBGNMBeAqr5nJvjHT9Q0QGAu8AdwD9gH7GmIewXGCvQe03/cvRNRh4APgfEXkHGA98ilUA1omPX0RuBL4HHhaRu+00p/3jDdAZqNPVCsPpsvkOq4X6X2PMOcD9WEEp66zGWYG2e7EM1mZjzPHGmHuwfpP/gtp9xkTkIeBLYDXQCuv56mSMKbKvPRE4BPxR6rizPF7LsGAS0jgAPYA8Y8yLWDfzW6wagBOrdvcSgP0Q+IC9MdI1HbhARE6xa1Jf2rrWYD2UdRXxsayuqVj3ywe8jNUPEngW3gV21tGPJZyuq7Bq5ScC7wPnGGO+AboDe+pAE1gF7a1YLal7RSTNGOMTi8B96gQsARCRIUGtsTrVBWCMOQy8bIx52d4+CKwAdtaBpkjaCoBjsVoQAf4L/CbWevG1yQHgdmPMK8BDto6A2zTgxnwZGAB0FpE/Sd0NFonXMqyYRDUOS4BkETnNGOMHfgK2A1cCTwL9ROReezTCuYA/hrqygcuDM4lIH6xO6R0x1LUduB2r42svcIeI/AF4A1hnjPHESNdW4A5jTJ4xZpYx5oCInAEIcKQONGH7e380xszDKmSftHc5bJ0AfYD2IjIZuBnwxkqXiDhsg4C9fQrwP8CW2tYUSZvN91i14T+KyAVYAw7WBWrwtciXwAIRSbaf541Ynb0YY7z2+yKs8mERcBdQF889xG8ZVkxcG4cKOorcWOtNXwXF/uDlQBu7pnIrkIrlvnnZGPN1jHVlikgTscZ6f4U1WuJVu0YcK12/Am3tB/N+LLfXbcCYQA00RrqWUXK/GovIc1jur/eNMZvrSBdBxnEUlj+/tzHGZx/XAusHewnwgTHmWmNMjRn6Kujy28c1EZGxWLF3xhhjPqopTVXVFpT+J6A/Vt/WGLvGXKuajDGHjEVgFN7JWAVw8XEi8kc7/W5jzLm18Iw1KrMd6GOJaRlWKYwxcfnCGgL3CNC0nP0XYhUal9nb3bCsf0oc6loY0AXcEI+64vh+DYiFrjJ5RwDv2J/PsN9vizNdZ9rvF8Xqf1nRPcNqdcVEE1arsxcwJWi7h/25XS3er8fsMulZ4Do7zRm0PyZlWGVfcddyEJE0ERmB5X87CWt0QzgWAj8AI8UaftkHq1aQEYe6silpztZoba4GdMX7/fopRrqCGQkMEZEc4HLbjTM+znRdZneaT69JXTWk7VIsX3qdaxIRMVbJ2xjYag8QWQZcZP8fs2tSl33NY0TkY6Arlut2OfCQiDQ3Vt9VoNyt0zIsamJtnYKsaCP73YV1k9KxJhs9BbSPYJ3fw/Jz9lddqqumdGHVMFsCk7F+yGc3FF3xqq0az9cILL/9J8C5tXy/GgE3BqW3whrocVI5x9Xqs1/l7xNzAda43rexeuuvw/KDB/b1sG/a1YC77EMY9DlNdamumtQVlCcJGIECYzAAAAZsSURBVNRQdMWrtuo+X1jRAO6og/t1PdDSTnfY7y2wOqBbhNNmf67xZ7+6r3hwKz2LNVTreazx9s8Hdhhj1gKLgfOwmmgE7TNBn/NUl+qqSV1Q7JIoMsZMbUC64lVblTXZeaYbY96pYU3htJ1JyTDUwAijFsAOY0ypoet18OxXi5gaB7Gm+LuBZ4wxs7CGVTYWkQeCsn2A1YQ8QURulzoIlqW6VBfU3gSteNUVr9qqo6k271UU2joCu+z8l4sdATneialxMMYcxZq6/gd7+wDwAlaohECH5F6s+QBjgWFYgc9Ul+pSXQ1EWzxqikYb8DsgRawZ/w9Td3Mpqkdd+K6wxuseVybNab+fjRWGIN3eTsGaiHWDvX0y1sSoh1SX6lJd9VdbPGqqprab7O3JwCbg3trQVluvWm85iMg9WEPHyjaljO2fnIvVWfMCFE+391ESWmIj0N0Y85LqUl2qq35qi0dNNaAtEPLiXaCPMebNmtZWq9SW1cHqhPkMmAecEM7i2p+Pwxr6lYU1Q/cyrM6lC1SX6lJd9VtbPGqqQW21NiGxLl61cUNd9rsbKyTurfZ2K+A0SppixwIfA1/a2xcAQ+1/xB9Ul+pSXfVXWzxqSgRtdfkKjAGuNmJFOXzWvqFTjDHficg5wD1YPrjjsXyCu7E6jdKxQiPU6ipfqkt1NURd8aotHjUlgrZYUCPGQUQEKz57I6yJILcDE40xb4i1aEsr4P8BzbGWCTzdGHNr0PFOYwc1q0lUl+pqiLriVVs8akoEbbHCFTlLpcjEGi1wsTHmqIjsA64QkauMMU+LiNtYkRl/E5HtQFfbShvAX4s3VXWproaoK161xaOmRNAWE2pktJIx5gglnTFgxSZfCFwoIsfYNxWxFgC5CThijPEaY3ympvxaqkt1qa641haPmhJBW6yoyaGsk7AWgm9jrOUUlwOFQBuxeAL4BVhrjHmsBq+rulSX6kocbfGoKRG01Tk1aRzmAvuxLa8xZglWHPd027IuAC4xxjxeg9dUXapLdSWWtnjUlAja6pya6nPAGLNLRL4AnhWRjVhNsgLs5RONMd/W1LVUl+pSXYmpLR41JYK2mGBqfozwJcA4YC1wf02fX3WpLtWV+NriUVMiaKvLV43NcwhGRNyW3TG1vuh6NKiu6FBd0RGvuiA+tcWjpgDxrK2uqBXjoCiKoiQ28bDYj6IoihJnqHFQFEVRQlDjoCiKooSgxkFRFEUJQY2DoiiKEoIaB0WpBiLSUURurMJx40Xkmiocd5uItI32OEWJFjUOimJjR9mMlo5A1MahGtwGqHFQah01DkqDQkRuEZHlIrJMRN6za/CjRWQW8JyIpIvIOBFZKCK/isgQ+7iOIvKjiCyxX2fZp3wWOEdElorIQyLiFJFR9vHLxVp/GDtw22sislpEvsFaH6AinY/b51gpImPt468B+gIf2NdLrb07pTR0dBKc0mAQkROAz7FW79onIs2A0VhrBQ8xxvhE5BlgtTHmfRFpghVs7RRK4vYXiEhX4CNjTF8ROQ8YaowZbF/jbqCVsdYASMYK/XytfY4/A4OA1sBq4E5jzGflaG1mjDlgf34P+NQY85WIzLavt6gWbpGiFFNjgfcUJQH4PfCZMWYfgDHmgIgATDAli7UMxFrkZai9nYK1gPxO4DURORnwAd3KucZAoE9Qf0JjoCtwLpZB8QE7ReT7CFrPF5G/AmlAM2AV8FVU31ZRqoEaB6UhIVgtgLLklsnzB2PMulIHijyJtXbwSVju2IIKrvG/xpjvyhx/aTnXDj2BSArwOtDXGLPdvnZKZY5VlJpC+xyUhsRM4DoRaQ6W6yZMnu+A/xW7SSEip9jpjYFdxhg/8CfAaacfxVpiMvj4P9uB2xCRbiKSDswB/mj3SbQBzq9AZ8AQ7BORDKw1iwOUvZ6i1AraclAaDMaYVSLyD+AHEfEBv4bJNhJ4mf/f3h2jIAxDcRj/3lE8hquzg4tn8RxugksvIB6heAHBujl1d3HrItQhFaEPRBddvt8YEghZ/slLINAMAdECc8pOfhcRS6DmddpogHtEnIAKWFNeMB2H8VdgQfllbAacgQtweDPPW0Rsh74t5V+BpwrYREQHTPu+775aBOlDXkhLkhLLSpKkxLKS9EcRsQcmo+bV+EJb+jXLSpKkxLKSJCkxHCRJieEgSUoMB0lSYjhIkpIHs64dBlMm2XQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2019-05-01 00:34:48 1 1694.47 1694.47 1694.47 1694.0 2019-05-01 00:34:48 可能为异常值\n",
    "df['2019-5-1'][['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看连续几天的数据情况\n",
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='created_at', length=865)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每天情况差不多，看看周末和平常是不是一样\n",
    "# 查看是不是周末的方法 0-周一，1-周二，2-周三，以此类推\n",
    "df['2019-5-2'].index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列表示工作日和周末\n",
    "df['weekdays'] = df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg           created_at  weekdays  weekend  \n",
       "created_at                                                                 \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07         3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07         3    False  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断是否是周末，是不是5,6\n",
    "# 增加一列判断是不是周末\n",
    "df['weekend'] = df['weekdays'].isin({5,6})\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对工作日、周末进行分组，对count列求平均值\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周末调用的平均次数多\n",
    "# 看周末哪个时段调用次数多\n",
    "df.groupby([df['weekend'],df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将周末非周末具体时间对比绘制成图标\n",
    "df.groupby([df['weekend'],df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周末和非周末时间对比\n",
    "df.groupby([df['weekend'],df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby([df['weekend'],df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
